Dr. Tian is an Education-Focused Lecturer with the Department of Electrical and Computer Systems Engineering in the Faculty of Engineering at Monash University in Melbourne, Australia.

Recent research includes designing competency-based assessment to align with the Engineers Australia attributes for electrical common core units. 

His research expertise lies in first year education, designing assessment at scale, and in digital health.

His previous research impacts the fields of health informatics, contextual modelling and pain management. Some previous work include the design and development of a clinical desicion support system for community health workers in collaboration with clinicians from The Alfred, where it bridged the gap between healthcare providers and rural populations, emphasizing preventive care and timely interventions. Other works include his doctoral thesis which introduced a high frequency contextual data collection approach and model which helped identify multidisciplinary factors influencing low back pain.

His interdisciplinary approach combines data science, patient-reported outcomes and clinical expertise, and extends beyond academia, as evidenced by collaborations with industry partners and usability studies of self-management apps.

He has a background in systems analysis, design and development; systems administration (Windows / Unix) and some experience in corporate management. 

His research interests are in:

• Authentic Assessment at scale for Engineering Education
• Teamwork satisfaction and automation
• Digital Health (Chronic or long-term management of conditions)

His current research includes:

• challenges of large-scale education using scalable competency assessment
• studying the impact of scaffolding in project based learning
• generative AI use in engineering
• using generative AI for student support and semi-automated feedback
• creating optimal engineering student teams

His current supervision interests include areas of:

• first year education
• team forming attributes, automation and student outcomes / satisfaction 
• health informatics / digital health